{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 Backpropagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow Walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from tensorflow.python.ops import nn_ops, gen_nn_ops\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from models.models_3_2 import MNIST_CNN\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "\n",
    "images = mnist.train.images\n",
    "labels = mnist.train.labels\n",
    "\n",
    "logdir = './tf_logs/3_2_DC/'\n",
    "ckptdir = logdir + 'model'\n",
    "\n",
    "if not os.path.exists(logdir):\n",
    "    os.mkdir(logdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Building Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('Classifier'):\n",
    "\n",
    "    # Initialize neural network\n",
    "    DNN = MNIST_CNN('CNN')\n",
    "\n",
    "    # Setup training process\n",
    "    X = tf.placeholder(tf.float32, [None, 784], name='X')\n",
    "    Y = tf.placeholder(tf.float32, [None, 10], name='Y')\n",
    "\n",
    "    activations, logits = DNN(X)\n",
    "    \n",
    "    tf.add_to_collection('BP', X)\n",
    "    tf.add_to_collection('BP', logits)\n",
    "    \n",
    "    for activation in activations:\n",
    "        tf.add_to_collection('BP', activation)\n",
    "\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))\n",
    "\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost, var_list=DNN.vars)\n",
    "\n",
    "    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "cost_summary = tf.summary.scalar('Cost', cost)\n",
    "accuray_summary = tf.summary.scalar('Accuracy', accuracy)\n",
    "summary = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Building Subgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('Prototype'):\n",
    "    \n",
    "    lmda = tf.placeholder(tf.float32, [], name='lambda')\n",
    "    X_prototype = tf.get_variable('X_prototype', shape=[10, 784], initializer=tf.constant_initializer(0.))\n",
    "\n",
    "    _, logits_prototype = DNN(X_prototype, reuse=True)\n",
    "    \n",
    "    cost_prototype = 1 - tf.trace(logits_prototype) + lmda * tf.nn.l2_loss(X_prototype)\n",
    "\n",
    "    optimizer_prototype = tf.train.AdamOptimizer().minimize(cost_prototype, var_list=[X_prototype])\n",
    "\n",
    "# Add the subgraph nodes to a collection so that they can be used after training of the network\n",
    "tf.add_to_collection('prototype', lmda)\n",
    "tf.add_to_collection('prototype', X_prototype)\n",
    "tf.add_to_collection('prototype', logits_prototype)\n",
    "tf.add_to_collection('prototype', cost_prototype)\n",
    "tf.add_to_collection('prototype', optimizer_prototype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 0.169085310 accuracy = 0.946727280\n",
      "Epoch: 0002 cost = 0.044592938 accuracy = 0.986218191\n",
      "Epoch: 0003 cost = 0.028531287 accuracy = 0.991000008\n",
      "Epoch: 0004 cost = 0.024309815 accuracy = 0.992200007\n",
      "Epoch: 0005 cost = 0.016091269 accuracy = 0.994600005\n",
      "Epoch: 0006 cost = 0.013665342 accuracy = 0.995690913\n",
      "Epoch: 0007 cost = 0.013399836 accuracy = 0.995763640\n",
      "Epoch: 0008 cost = 0.010240791 accuracy = 0.996636367\n",
      "Epoch: 0009 cost = 0.010054544 accuracy = 0.996763639\n",
      "Epoch: 0010 cost = 0.008347637 accuracy = 0.997290912\n",
      "Epoch: 0011 cost = 0.008589237 accuracy = 0.997090912\n",
      "Epoch: 0012 cost = 0.008013549 accuracy = 0.997472730\n",
      "Epoch: 0013 cost = 0.005356289 accuracy = 0.998181820\n",
      "Epoch: 0014 cost = 0.004136225 accuracy = 0.998800001\n",
      "Epoch: 0015 cost = 0.006400488 accuracy = 0.997945457\n",
      "Accuracy: 0.9862\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n",
    "\n",
    "# Hyper parameters\n",
    "training_epochs = 15\n",
    "batch_size = 100\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    total_batch = int(mnist.train.num_examples / batch_size)\n",
    "    avg_cost = 0\n",
    "    avg_acc = 0\n",
    "    \n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        \n",
    "        # zero-center the images\n",
    "        mean = np.mean(batch_xs, axis=0, keepdims=True)\n",
    "        batch_xs = batch_xs - mean\n",
    "        \n",
    "        _, c, a, summary_str = sess.run([optimizer, cost, accuracy, summary], feed_dict={X: batch_xs, Y: batch_ys})\n",
    "        avg_cost += c / total_batch\n",
    "        avg_acc += a / total_batch\n",
    "        \n",
    "        file_writer.add_summary(summary_str, epoch * total_batch + i)\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost), 'accuracy =', '{:.9f}'.format(avg_acc))\n",
    "    \n",
    "    saver.save(sess, ckptdir)\n",
    "\n",
    "print('Accuracy:', sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Restoring Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./tf_logs/2_4_DC/model\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "new_saver = tf.train.import_meta_graph(ckptdir + '.meta')\n",
    "new_saver.restore(sess, tf.train.latest_checkpoint(logdir))\n",
    "\n",
    "activations = tf.get_collection('BP')\n",
    "X = activations[0]\n",
    "logits = activations[1]\n",
    "\n",
    "prototype = tf.get_collection('prototype')\n",
    "lmda = prototype[0]\n",
    "X_prototype = prototype[1]\n",
    "cost_prototype = prototype[3]\n",
    "optimizer_prototype = prototype[4]\n",
    "\n",
    "sample_imgs = [images[np.argmax(labels, axis=1) == i][5] for i in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Class Model Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 00000 Cost = 2.348793745\n",
      "Epoch: 00500 Cost = -817.845092773\n",
      "Epoch: 01000 Cost = -1951.692382812\n",
      "Epoch: 01500 Cost = -2955.781494141\n",
      "Epoch: 02000 Cost = -3905.116455078\n",
      "Epoch: 02500 Cost = -4824.121093750\n",
      "Epoch: 03000 Cost = -5725.052246094\n",
      "Epoch: 03500 Cost = -6610.922851562\n",
      "Epoch: 04000 Cost = -7481.562011719\n",
      "Epoch: 04500 Cost = -8331.448242188\n"
     ]
    }
   ],
   "source": [
    "img_means = []\n",
    "for i in range(10):\n",
    "    img_means.append(np.mean(images[np.argmax(labels, axis=1) == i], axis=0))\n",
    "\n",
    "for epoch in range(5000):\n",
    "    _, c = sess.run([optimizer_prototype, cost_prototype], feed_dict={lmda: 0.01})\n",
    "    \n",
    "    if epoch % 500 == 0:\n",
    "        print('Epoch: {:05d} Cost = {:.9f}'.format(epoch, np.sum(c)))\n",
    "    \n",
    "X_prototypes = sess.run(X_prototype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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pzaBBg8z8hBNOMPNHHnmE3ld9SGJOofd+FIBR2W+NSMO1Zs0a3HvvvTVyth8C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pX0ttIQWo/TmV6RBIX20o+IiEhcQutxWvSKJCi0o2AREZG4hNbjtOgVSUiI41xERETiEGKP06JX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\nWjNkyBAzZ+NuAODf/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aI1w4YNM/Pf/va3tCZtOtIrIv+x6/QP+5OARwHcDCCuLY+IiNR4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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253b0eb5048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "for i in range(5):\n",
    "    plt.subplot(5, 2, 2 * i + 1)\n",
    "    plt.imshow(np.reshape(X_prototypes[2 * i], [28, 28]), cmap='gray', interpolation='none')\n",
    "    plt.title('Digit: {}'.format(2 * i))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.colorbar()\n",
    "    \n",
    "    plt.subplot(5, 2, 2 * i + 2)\n",
    "    plt.imshow(np.reshape(X_prototypes[2 * i + 1], [28, 28]), cmap='gray', interpolation='none')\n",
    "    plt.title('Digit: {}'.format(2 * i + 1))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.colorbar()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 7. Image-Specific Class Saliency Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vo74hdwx+q/NZSWskPauOr4y8uNrNAQCgFNQ3ZI3LHgAAANBvcOYX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\nye+yk+po5N337LPPujle54RURwWvZo4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Ue61TZ0u91mWpcViVulHjWkIIMwusYoGkqSGE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8lM2FxOd3d3Mp7r3HDkyJFkPNdNYNasWcl4NKtWio9iom4GkrRo0aJkfPfu3W0/T+6DtHjx4mT8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\nAB3j/wOYQsXNwwCIXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253bb1d3080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saliency = tf.abs(tf.gradients(logits, X))\n",
    "\n",
    "hmaps = np.squeeze(sess.run(saliency, feed_dict={X: sample_imgs}), axis=0)\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "for i in range(5):\n",
    "    plt.subplot(5, 2, 2 * i + 1)\n",
    "    plt.imshow(np.reshape(hmaps[2 * i], [28, 28]), cmap='gray', interpolation='none')\n",
    "    plt.title('Digit: {}'.format(2 * i))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.colorbar()\n",
    "    \n",
    "    plt.subplot(5, 2, 2 * i + 2)\n",
    "    plt.imshow(np.reshape(hmaps[2 * i + 1], [28, 28]), cmap='gray', interpolation='none')\n",
    "    plt.title('Digit: {}'.format(2 * i + 1))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.colorbar()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
